Merge pull request #3433 from pipecat-ai/mb/port-realtime-examples-transcript-events

Update examples to use transcription events from context aggregators

examples/foundational/19-openai-realtime.py

@@ -15,14 +15,17 @@ from loguru import logger
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, LLMSetToolsFrame, TranscriptionMessage
+from pipecat.frames.frames import LLMRunFrame, LLMSetToolsFrame
 from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.llm_response_universal import (
-from pipecat.processors.transcript_processor import TranscriptProcessor
+    AssistantTurnStoppedMessage,
+    LLMContextAggregatorPair,
+    UserTurnStoppedMessage,
+)
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.llm_service import FunctionCallParams
@@ -177,8 +180,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
     llm.register_function("get_news", get_news)
 
-    transcript = TranscriptProcessor()
-
     # Create a standard OpenAI LLM context object using the normal messages format. The
     # OpenAIRealtimeLLMService will convert this internally to messages that the
     # openai WebSocket API can understand.
@@ -189,15 +190,16 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
 
     context_aggregator = LLMContextAggregatorPair(context)
 
+    user_aggregator = context_aggregator.user()
+    assistant_aggregator = context_aggregator.assistant()
+
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
-            context_aggregator.user(),
+            user_aggregator,
-            transcript.user(),  # LLM pushes TranscriptionFrames upstream
             llm,  # LLM
             transport.output(),  # Transport bot output
-            transcript.assistant(),  # After the transcript output, to time with the audio output
+            assistant_aggregator,
-            context_aggregator.assistant(),
         ]
     )
 
@@ -238,14 +240,18 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    # Register event handler for transcript updates
+    # Log transcript updates
-    @transcript.event_handler("on_transcript_update")
+    @user_aggregator.event_handler("on_user_turn_stopped")
-    async def on_transcript_update(processor, frame):
+    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
-        for msg in frame.messages:
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
-            if isinstance(msg, TranscriptionMessage):
+        line = f"{timestamp}user: {message.content}"
-                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+        logger.info(f"Transcript: {line}")
-                line = f"{timestamp}{msg.role}: {msg.content}"
+
-                logger.info(f"Transcript: {line}")
+    @assistant_aggregator.event_handler("on_assistant_turn_stopped")
+    async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}assistant: {message.content}"
+        logger.info(f"Transcript: {line}")
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 

examples/foundational/19b-openai-realtime-beta-text.py

@@ -14,12 +14,11 @@ from loguru import logger
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
+from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -157,8 +156,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
     llm.register_function("get_current_weather", fetch_weather_from_api)
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
 
-    transcript = TranscriptProcessor()
-
     # Create a standard OpenAI LLM context object using the normal messages format. The
     # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
     # openai WebSocket API can understand.
@@ -175,9 +172,7 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
             context_aggregator.user(),
             llm,  # LLM
             tts,  # TTS
-            transcript.user(),  # Placed after the LLM, as LLM pushes TranscriptionFrames downstream
             transport.output(),  # Transport bot output
-            transcript.assistant(),  # After the transcript output, to time with the audio output
             context_aggregator.assistant(),
         ]
     )
@@ -202,15 +197,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    # Register event handler for transcript updates
-    @transcript.event_handler("on_transcript_update")
-    async def on_transcript_update(processor, frame):
-        for msg in frame.messages:
-            if isinstance(msg, TranscriptionMessage):
-                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
-                line = f"{timestamp}{msg.role}: {msg.content}"
-                logger.info(f"Transcript: {line}")
-
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
     await runner.run(task)

examples/foundational/19b-openai-realtime-text.py

@@ -14,13 +14,12 @@ from loguru import logger
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
+from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -164,8 +163,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
     llm.register_function("get_current_weather", fetch_weather_from_api)
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
 
-    transcript = TranscriptProcessor()
-
     # Create a standard OpenAI LLM context object using the normal messages format. The
     # OpenAIRealtimeLLMService will convert this internally to messages that the
     # openai WebSocket API can understand.
@@ -180,11 +177,9 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         [
             transport.input(),  # Transport user input
             context_aggregator.user(),
-            transcript.user(),  # LLM pushes TranscriptionFrames upstream
             llm,  # LLM
             tts,  # TTS
             transport.output(),  # Transport bot output
-            transcript.assistant(),  # After the transcript output, to time with the audio output
             context_aggregator.assistant(),
         ]
     )
@@ -209,15 +204,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    # Register event handler for transcript updates
-    @transcript.event_handler("on_transcript_update")
-    async def on_transcript_update(processor, frame):
-        for msg in frame.messages:
-            if isinstance(msg, TranscriptionMessage):
-                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
-                line = f"{timestamp}{msg.role}: {msg.content}"
-                logger.info(f"Transcript: {line}")
-
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
     await runner.run(task)

examples/foundational/28-transcription-processor.py

@@ -1,209 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-from typing import List, Optional
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage, TranscriptionUpdateFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.processors.transcript_processor import TranscriptProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
-from pipecat.turns.user_turn_strategies import UserTurnStrategies
-
-load_dotenv(override=True)
-
-
-class TranscriptHandler:
-    """Handles real-time transcript processing and output.
-
-    Maintains a list of conversation messages and outputs them either to a log
-    or to a file as they are received. Each message includes its timestamp and role.
-
-    Attributes:
-        messages: List of all processed transcript messages
-        output_file: Optional path to file where transcript is saved. If None, outputs to log only.
-    """
-
-    def __init__(self, output_file: Optional[str] = None):
-        """Initialize handler with optional file output.
-
-        Args:
-            output_file: Path to output file. If None, outputs to log only.
-        """
-        self.messages: List[TranscriptionMessage] = []
-        self.output_file: Optional[str] = output_file
-        logger.debug(
-            f"TranscriptHandler initialized {'with output_file=' + output_file if output_file else 'with log output only'}"
-        )
-
-    async def save_message(self, message: TranscriptionMessage):
-        """Save a single transcript message.
-
-        Outputs the message to the log and optionally to a file.
-
-        Args:
-            message: The message to save
-        """
-        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
-        line = f"{timestamp}{message.role}: {message.content}"
-
-        # Always log the message
-        logger.info(f"Transcript: {line}")
-
-        # Optionally write to file
-        if self.output_file:
-            try:
-                with open(self.output_file, "a", encoding="utf-8") as f:
-                    f.write(line + "\n")
-            except Exception as e:
-                logger.error(f"Error saving transcript message to file: {e}")
-
-    async def on_transcript_update(
-        self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
-    ):
-        """Handle new transcript messages.
-
-        Args:
-            processor: The TranscriptProcessor that emitted the update
-            frame: TranscriptionUpdateFrame containing new messages
-        """
-        logger.debug(f"Received transcript update with {len(frame.messages)} new messages")
-
-        for msg in frame.messages:
-            self.messages.append(msg)
-            await self.save_message(msg)
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative, helpful, and brief way. Say hello.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(
-            user_turn_strategies=UserTurnStrategies(
-                stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
-            ),
-        ),
-    )
-
-    # Create transcript processor and handler
-    transcript = TranscriptProcessor()
-    transcript_handler = TranscriptHandler()  # Output to log only
-    # transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            transcript.user(),  # User transcripts
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            transcript.assistant(),  # Assistant transcripts
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Start conversation - empty prompt to let LLM follow system instructions
-        await task.queue_frames([LLMRunFrame()])
-
-    # Register event handler for transcript updates
-    @transcript.event_handler("on_transcript_update")
-    async def on_transcript_update(processor, frame):
-        await transcript_handler.on_transcript_update(processor, frame)
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/40-aws-nova-sonic.py

@@ -21,7 +21,11 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.llm_response_universal import (
+    AssistantTurnStoppedMessage,
+    LLMContextAggregatorPair,
+    UserTurnStoppedMessage,
+)
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService
@@ -154,14 +158,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     )
     context_aggregator = LLMContextAggregatorPair(context)
 
+    user_aggregator = context_aggregator.user()
+    assistant_aggregator = context_aggregator.assistant()
+
     # Build the pipeline
     pipeline = Pipeline(
         [
             transport.input(),
-            context_aggregator.user(),
+            user_aggregator,
             llm,
             transport.output(),
-            context_aggregator.assistant(),
+            assistant_aggregator,
         ]
     )
 
@@ -192,6 +199,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client disconnected")
         await task.cancel()
 
+    @user_aggregator.event_handler("on_user_turn_stopped")
+    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}user: {message.content}"
+        logger.info(f"Transcript: {line}")
+
+    @assistant_aggregator.event_handler("on_assistant_turn_stopped")
+    async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}assistant: {message.content}"
+        logger.info(f"Transcript: {line}")
+
     # Run the pipeline
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
     await runner.run(task)

examples/foundational/42-interruption-config.py

@@ -13,6 +13,7 @@ from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnal
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import LLMRunFrame
+from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -21,7 +22,6 @@ from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
     LLMUserAggregatorParams,
 )
-from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -36,6 +36,7 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
 
 load_dotenv(override=True)
 
+
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets
 # selected.
@@ -70,8 +71,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
-    transcript = TranscriptProcessor()
-
     messages = [
         {
             "role": "system",
@@ -94,7 +93,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         [
             transport.input(),  # Transport user input
             stt,
-            transcript.user(),  # User transcripts
             context_aggregator.user(),  # User responses
             llm,  # LLM
             tts,  # TTS
@@ -110,6 +108,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_usage_metrics=True,
         ),
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+        observers=[TranscriptionLogObserver()],
     )
 
     @transport.event_handler("on_client_connected")
@@ -124,12 +123,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    # Register event handler for transcript updates
-    @transcript.event_handler("on_transcript_update")
-    async def on_transcript_update(processor, frame):
-        for message in frame.messages:
-            logger.info(f"Transcription [{message.role}]: {message.content}")
-
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
     await runner.run(task)

examples/foundational/51-grok-realtime.py

@@ -36,7 +36,7 @@ from pipecat.adapters.schemas.tools_schema import ToolsSchema
 
 # Note: Grok has built-in server-side VAD, so we don't need local VAD
 # from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
+from pipecat.frames.frames import LLMRunFrame
 from pipecat.observers.loggers.transcription_log_observer import (
     TranscriptionLogObserver,
 )
@@ -45,9 +45,10 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
+    AssistantTurnStoppedMessage,
     LLMContextAggregatorPair,
+    UserTurnStoppedMessage,
 )
-from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.grok.realtime.events import (
@@ -208,9 +209,6 @@ Always be helpful and proactive in offering assistance.""",
     llm.register_function("get_current_time", get_current_time)
     llm.register_function("get_restaurant_recommendation", get_restaurant_recommendation)
 
-    # Create transcript processor for logging
-    transcript = TranscriptProcessor()
-
     # Create context with initial message and tools
     context = LLMContext(
         [{"role": "user", "content": "Say hello and introduce yourself!"}],
@@ -219,18 +217,19 @@ Always be helpful and proactive in offering assistance.""",
 
     context_aggregator = LLMContextAggregatorPair(context)
 
+    user_aggregator = context_aggregator.user()
+    assistant_aggregator = context_aggregator.assistant()
+
     # Build the pipeline
     # Note: In realtime mode, transcription comes from Grok (upstream),
     # so transcript.user() goes BEFORE llm
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input (audio)
-            context_aggregator.user(),
+            user_aggregator,
-            transcript.user(),  # Transcription from Grok goes upstream
             llm,  # Grok Realtime LLM (handles STT + LLM + TTS)
             transport.output(),  # Transport bot output (audio)
-            transcript.assistant(),  # Log assistant speech
+            assistant_aggregator,
-            context_aggregator.assistant(),
         ]
     )
 
@@ -256,13 +255,17 @@ Always be helpful and proactive in offering assistance.""",
         await task.cancel()
 
     # Log transcript updates
-    @transcript.event_handler("on_transcript_update")
+    @user_aggregator.event_handler("on_user_turn_stopped")
-    async def on_transcript_update(processor, frame):
+    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
-        for msg in frame.messages:
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
-            if isinstance(msg, TranscriptionMessage):
+        line = f"{timestamp}user: {message.content}"
-                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+        logger.info(f"Transcript: {line}")
-                line = f"{timestamp}{msg.role}: {msg.content}"
+
-                logger.info(f"Transcript: {line}")
+    @assistant_aggregator.event_handler("on_assistant_turn_stopped")
+    async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}assistant: {message.content}"
+        logger.info(f"Transcript: {line}")
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)